This paper addresses the problem of learning multiple spoken language
understanding (SLU) tasks that have overlapping sets of slots. In such a
scenario, it is possible to achieve better slot filling performance by learning
multiple tasks simultaneously, as opposed to learning them independently. We
focus on presenting a number of simple multi-task learning algorithms for slot
filling systems based on semi-Markov CRFs, assuming the knowledge of shared
slots. Furthermore, we discuss an intradomain clustering method that
automatically discovers shared slots from training data. The effectiveness of our
proposed approaches is demonstrated in an SLU application that involves three
different yet related tasks.